对比13种模型在自身免疫疾病病理图像上的表现，发现癌症预训练模型并不占优。
Going Beyond H&E and Oncology: How Do Histopathology Foundation Models Perform for Multi-stain IHC and Immunology?
- 用注意力多实例学习分类器评估模型迁移能力
- 癌症H&E图像预训练模型在自身免疫病中表现不优于ImageNet模型
- 发现模型存在特征误判和重要性偏差，适合关注跨领域泛化的研究者
本研究评估了当前最先进的组织病理学基础模型在分布外的多染色自身免疫性免疫组化数据集上的泛化能力。我们比较了13种特征提取模型，包括ImageNet预训练网络以及在公开和私有数据上训练的组织病理学基础模型，在类风湿性关节炎分型和干燥综合征检测任务中的表现。通过一个简单的基于注意力的多实例学习分类器，评估癌症H&E图像学习表征向自身免疫性IHC图像的迁移能力。出乎意料的是，组织病理学预训练模型并未显著优于ImageNet预训练模型。此外，观察到自身免疫特征被误解释及特征重要性存在偏差。结果凸显了从癌症病理学向自身免疫病理学知识迁移的挑战，并强调需对AI模型在多样化组织病理学任务中进行审慎评估。代码已开源：https://github.com/AmayaGS/ImmunoHistoBench。
原文摘要 · Abstract (English)
This study evaluates the generalisation capabilities of state-of-the-art histopathology foundation models on out-of-distribution multi-stain autoimmune Immunohistochemistry datasets. We compare 13 feature extractor models, including ImageNet-pretrained networks, and histopathology foundation models trained on both public and proprietary data, on Rheumatoid Arthritis subtyping and Sjogren's Disease detection tasks. Using a simple Attention-Based Multiple Instance Learning classifier, we assess the transferability of learned representations from cancer H&E images to autoimmune IHC images. Contrary to expectations, histopathology-pretrained models did not significantly outperform ImageNet-pretrained models. Furthermore, there was evidence of both autoimmune feature misinterpretation and biased feature importance. Our findings highlight the challenges in transferring knowledge from cancer to autoimmune histopathology and emphasise the need for careful evaluation of AI models across diverse histopathological tasks. The code to run this benchmark is available at https://github.com/AmayaGS/ImmunoHistoBench.
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